Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

481
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
481

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Atrial Fibrillation and Stroke Prevention and Management in Chronic Kidney Disease.

Clinical journal of the American Society of Nephrology : CJASN·2026
Same author

Utility of Risk Scores for Predicting Stroke and Intracranial Bleeding Across Levels of Kidney Function in Two Large Community-Based Cohorts of Older Adults With Atrial Fibrillation.

American journal of kidney diseases : the official journal of the National Kidney Foundation·2026
Same author

Patterns of antithrombotic treatment after left atrial appendage occlusion.

Heart rhythm·2026
Same author

Incidence, Risk Factors, and Outcomes in Stressor-Associated Atrial Fibrillation: Insights From the VITAL-AF Trial.

Circulation·2026
Same author

Association of Recurrent Atrial Fibrillation With Subsequent Kidney Function Decline in Adults Receiving Rhythm Control Therapy.

American journal of kidney diseases : the official journal of the National Kidney Foundation·2026
Same author

Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial.

NPJ digital medicine·2025
Same journal

AHA/ACC/ESC/WHF Expert Consensus Document: Second Universal Definition of Heart Failure (2026).

Journal of the American College of Cardiology·2026
Same journal

Insights from the First Year of Program Signaling in Cardiovascular Disease Fellowship Recruitment.

Journal of the American College of Cardiology·2026
Same journal

The Role of Ammonia in Particle Toxicity.

Journal of the American College of Cardiology·2026
Same journal

Demystifying Arrhythmia Surveillance in Cardiac Amyloidosis With EXCALIBUR.

Journal of the American College of Cardiology·2026
Same journal

Combined LDL-C, Lp(a), and hsCRP Assessment Predicts ASCVD in the Multiethnic HELIUS Cohort.

Journal of the American College of Cardiology·2026
Same journal

Beyond a Single Pathway: LDL-C, Lipoprotein(a), and hsCRP for Life-Course ASCVD Prevention.

Journal of the American College of Cardiology·2026
See all related articles

Related Experiment Video

Updated: Apr 16, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

2.3K

Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF

Natasha A Vedage1, Sam F Friedman2, Yuchiao Chang3

  • 1Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA; Telemachus and Irene Demoulas Family Foundation Center for Cardiac Arrhythmias, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA.

Journal of the American College of Cardiology
|April 15, 2026
PubMed
Summary
This summary is machine-generated.

Screening for atrial fibrillation (AF) using artificial intelligence (AI) risk models identified high-risk individuals, improving detection efficiency. This risk-based approach shows promise for targeted AF screening in primary care.

Keywords:
artificial intelligenceatrial fibrillationelectrocardiogramrandomized trialrisk predictionscreening

More Related Videos

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.2K
The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation
23:33

The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation

Published on: February 28, 2012

84.8K

Related Experiment Videos

Last Updated: Apr 16, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

2.3K
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.2K
The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation
23:33

The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation

Published on: February 28, 2012

84.8K

Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Preventive Healthcare

Background:

  • Current atrial fibrillation (AF) screening relies on age (≥65 years) with limited effectiveness.
  • Earlier AF detection and intervention can improve patient outcomes.
  • Novel risk stratification methods are needed to enhance screening yield.

Purpose of the Study:

  • To evaluate the effectiveness of artificial intelligence (AI)-based risk models in identifying individuals who benefit from AF screening.
  • To assess if AF screening yields are higher in individuals identified as high-risk by validated clinical and ECG-AI models.
  • To compare the performance of different risk models in predicting 2-year incident AF.

Main Methods:

  • The VITAL-AF cluster-randomized trial involved patients aged ≥65 years in primary care settings.
  • Risk prediction utilized the CHARGE-AF clinical score, an ECG-AI model, and a combined CH-AI model.
  • Two-year incident AF discrimination was assessed using AUROC and average precision; screening effect was evaluated across risk deciles.

Main Results:

  • The CH-AI model demonstrated strong discrimination for 2-year AF risk (AUROC: 0.788).
  • A significant AF screening effect was observed in the highest CH-AI risk decile (10.07/100 person-years vs. 7.76 in control, P < 0.05).
  • The number needed to screen was 43 per year in the top risk decile.

Conclusions:

  • ECG-based AI and clinical factors effectively identify high-risk individuals for AF screening.
  • A risk-based screening strategy may increase efficiency but reduce population coverage.
  • Further research is needed to optimize risk-based AF screening, considering additional clinical and system factors.